image/svg+xml Further work Concrete Self sealing Sukhbaatar BatchuluunSupervisor: Koichiro ShiomoriInterdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki 宮崎大学 University of Miyazaki Preparation of polystyrene microcapsules containing water droplets by solvent evaporation method and their structural distribution analysis by machine learning 1.Introduction 2.Experimental 3.Result and discusions 4.Conclusion ...- The structures were automatically detected by the ... and were classified by SVM.- The structural distribution of the microcapsules prepared at a high weight ratio of solid to the organic phase and a lower ratio of that were analyzed based on classification results. ... Figure 3.1.1. Microcapsule structure Figure 3.1.2. Comparisons of emulsion structural distribution in different conditions Figure 3.2.1. Diameter distributions of microcapsules. Shelf life may be increased Microencapsulatedingredients do not interferewith other ingredients Surface and colloidal propertiesof active agents can be altered Wider range of specificproducts for consumers tochoose from Controlled or/and sustained release of active agents Liquids and gases canbe changed to pseudo solid Protection of the active agent from environment Advantages No single technique for all active agents or product application More skill and knowledge are required to use this advancedand complex technology Possible cross-reaction thatmay occur between the coreand wall material selected Difficult to achievecontinuous and uniform film Disadvantages Organic solventevaporation Emulsification Homogenisation Filtration Characterization Data Collection Image Segmentation GC BC GR BR MoH MuH Train Test Feature extraction Classification & Validation P(r1) < P(r2) P(r1) ≈ P(r2) Mu Mo Production costs To vacuum 400C To vacuum (-30kPa) https://cvxbaatar.github.io/ https://qrgo.page.link/FZZ6o Figure 3.2.2. Relationships between water content and experimental condition. Figure 3.2.3. Relationships between diameter and experimental condition. Figure 2.1. Microcapsules preparation and structure classification. (Where: xi-feature vector, yi -label, p-feature vector dimension, n-number of input points, w, b-hyperplane parameters, ξ-slack variables, C-misclassifications parameter, F-feature space.) Crack Microcapsule 3.1. Structural distribution analysis by machine learning (ML) 3.2. Preparation condition of microcapsules ML based measurement results Manual measurement results ClassifierkNN kNN kNN kNN kNN kNN SVM SVM SVM SVM SVM SVM Accuracy, %80.83 64.27 82.57 59.91 40.96 69.72 71.24 51.85 83.01 45.32 55.77 65.36 Table 3.1.1. Accuracy of the proposed feature extraction techniques. kNN- k nearest neighborhood, SVM-support vector machine SVM was selected since it had the highest accuracy. 0.050.100.300.50 0 25 50 75 100 125 0 25 50 75 100 125 Log(Diameter) Count 0.050.100.300.50 0 25 50 75 100 125 0 25 50 75 100 125 Log(Diameter) Count 77% of accuracy 81% of accuracy Further work Concrete Self sealing Crack Microcapsule MachineLearning Microcapsule
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